Transcription factor prediction database (DBD)

Transcription factor prediction database (DBD) predicts sequence-specific DNA-binding transcription factors across publicly available proteomes to support comparative and evolutionary analysis of DNA-binding domain (DBD) families.

Key Features:

• Hidden Markov model prediction: Uses hidden Markov models to identify significant matches to sequence-specific DNA-binding domain families.
• Proteome coverage: Provides predictions across over 700 publicly available proteomes.
• Domain-centric annotation: Assigns DNA-binding domains to proteins and identifies DBD family membership.
• Gene and cross-database annotations: Records gene names and includes links to external databases for predicted transcription factors.
• Domain arrangement similarity: Reports transcription factors with similar domain arrangements.
• Comparative distribution analysis: Characterizes the distribution of DBD families across the tree of life, including distinctions between eukaryotic and prokaryotic expansions.

Scientific Applications:

• Evolutionary biology: Analyze DBD family distribution and expansion patterns to study transcriptional regulation evolution.
• Functional genomics: Annotate and classify sequence-specific DNA-binding transcription factors within proteomes for functional studies.
• Systems biology: Compare predicted transcription factor numbers relative to proteome size to infer mechanisms such as splice variant prevalence or combinatorial control strategies.

Methodology:

Hidden Markov models are applied to proteome sequences to detect significant matches to sequence-specific DNA-binding domain families.